09_time_series_models/05_cointegration_tests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cointegration Tests & Pairs Trading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is cointegration?\n",
    "We have seen how a time series can have a unit root that creates a stochastic trend and makes the time series highly persistent. When we use such an integrated time series in their original, rather than in differenced, form as a feature in a linear regression model, its relationship with the outcome will often appear statistically significant, even though it is not. This phenomenon is called spurious regression (for details, see Chapter 18 in [Wooldridge, 2008](https://economics.ut.ac.ir/documents/3030266/14100645/Jeffrey_M._Wooldridge_Introductory_Econometrics_A_Modern_Approach__2012.pdf)). Therefore, the recommended solution is to difference the time series so they become stationary before using them in a model.\n",
    "\n",
    "However, there is an exception when there are cointegration relationships between the outcome and one or more input variables. To understand the concept of cointegration, let's first remember that the residuals of a regression model are a linear combination of the inputs and the output series.\n",
    "\n",
    "Usually, the residuals of the regression of one integrated time series on one or more such series yields non-stationary residuals that are also integrated, and thus behave like a random walk. However, for some time series, this is not the case: the regression produces coefficients that yield a linear combination of the time series in the form of the residuals that are stationary, even though the individual series are not. Such time series are\n",
    "cointegrated.\n",
    "\n",
    "A non-technical example is that of a drunken man on a random walk accompanied by his dog (on a leash). Both trajectories are non-stationary but cointegrated because the dog will occasionally revert to his owner. In the trading context, arbitrage constraints imply cointegration between spot and futures prices.\n",
    "\n",
    "In other words, a linear combination of two or more cointegrated series has a stable mean to which this linear combination reverts. This also applies when the individual series are integrated of a higher order and the linear combination reduces the overall order of integration.\n",
    "\n",
    "Cointegration differs from correlation: two series can be highly correlated but need not be cointegrated. For example, if two growing series are constant multiples of each other, their correlation will be high, but any linear combination will also grow rather than revert to a stable mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cointegration for Pairs Trading\n",
    "\n",
    "Cointegration is very useful: if two or more asset price series tend to revert to a common mean, we can leverage deviations from the trend because they should imply future price moves in the opposite direction. The mathematics behind cointegration is more involved, so we will only focus on the practical aspects; for an in-depth treatment, see [Lütkepohl (2005)](https://www.springer.com/gp/book/9783540401728).\n",
    "\n",
    "In this notebook, we will address how we can identify pairs with such a long-term stationary relationship, estimate the expected time for any disequilibrium to correct, and how to utilize these tools to implement and backtest a long-short pairs trading strategy. There are two approaches to testing for cointegration:\n",
    "- The Engle-Granger two-step method\n",
    "- The Johansen test\n",
    "\n",
    "The book chapter discusses each test in turn; in this notebook we show how they help identify cointegrated securities that tend to revert to a common trend, a fact that we can leverage for a statistical arbitrage\n",
    "strategy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:37:17.768213Z",
     "start_time": "2021-02-23T14:37:17.765416Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:45.781616Z",
     "start_time": "2021-02-23T15:13:45.765250Z"
    }
   },
   "outputs": [],
   "source": [
    "from time import time\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm \n",
    "\n",
    "import numpy as np\n",
    "from numpy.linalg import LinAlgError\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.tree import  DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller, coint\n",
    "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
    "from statsmodels.tsa.api import VAR\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:37:18.698099Z",
     "start_time": "2021-02-23T14:37:18.695965Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', lambda x: f'{x:,.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:37:18.705831Z",
     "start_time": "2021-02-23T14:37:18.699717Z"
    }
   },
   "outputs": [],
   "source": [
    "DATA_PATH = Path('..', 'data')\n",
    "STORE = DATA_PATH / 'assets.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Johansen Test Critical Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:37:18.713695Z",
     "start_time": "2021-02-23T14:37:18.707298Z"
    }
   },
   "outputs": [],
   "source": [
    "critical_values = {0: {.9: 13.4294, .95: 15.4943, .99: 19.9349},\n",
    "                   1: {.9: 2.7055, .95: 3.8415, .99: 6.6349}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:37:18.721417Z",
     "start_time": "2021-02-23T14:37:18.714576Z"
    }
   },
   "outputs": [],
   "source": [
    "trace0_cv = critical_values[0][.95] # critical value for 0 cointegration relationships\n",
    "trace1_cv = critical_values[1][.95] # critical value for 1 cointegration relationship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load & Clean Stock & ETF Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove highly correlated assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:37:18.733836Z",
     "start_time": "2021-02-23T14:37:18.722277Z"
    }
   },
   "outputs": [],
   "source": [
    "def remove_correlated_assets(df, cutoff=.99):\n",
    "    corr = df.corr().stack()\n",
    "    corr = corr[corr < 1]\n",
    "    to_check = corr[corr.abs() > cutoff].index\n",
    "    keep, drop = set(), set()\n",
    "    for s1, s2 in to_check:\n",
    "        if s1 not in keep:\n",
    "            if s2 not in keep:\n",
    "                keep.add(s1)\n",
    "                drop.add(s2)\n",
    "            else:\n",
    "                drop.add(s1)\n",
    "        else:\n",
    "            keep.discard(s2)\n",
    "            drop.add(s2)\n",
    "    return df.drop(drop, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove stationary series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:37:18.745120Z",
     "start_time": "2021-02-23T14:37:18.734706Z"
    }
   },
   "outputs": [],
   "source": [
    "def check_stationarity(df):\n",
    "    results = []\n",
    "    for ticker, prices in df.items():\n",
    "        results.append([ticker, adfuller(prices, regression='ct')[1]])\n",
    "    return pd.DataFrame(results, columns=['ticker', 'adf']).sort_values('adf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:37:18.753562Z",
     "start_time": "2021-02-23T14:37:18.747783Z"
    }
   },
   "outputs": [],
   "source": [
    "def remove_stationary_assets(df, pval=.05):\n",
    "    test_result = check_stationarity(df)\n",
    "    stationary = test_result.loc[test_result.adf <= pval, 'ticker'].tolist()\n",
    "    return df.drop(stationary, axis=1).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:58:37.683658Z",
     "start_time": "2021-02-23T14:58:37.677577Z"
    }
   },
   "outputs": [],
   "source": [
    "def select_assets(asset_class='stocks', n=500, start=2010, end=2019):\n",
    "    idx = pd.IndexSlice\n",
    "    with pd.HDFStore(STORE) as store:\n",
    "        df = (pd.concat([store[f'stooq/us/nasdaq/{asset_class}/prices'],\n",
    "                         store[f'stooq/us/nyse/{asset_class}/prices']])\n",
    "              # stooq download can have duplicate assets\n",
    "              .loc[lambda df: ~df.index.duplicated()]\n",
    "              .sort_index()\n",
    "              .loc[idx[:, f'{start}':f'{end}'], :]\n",
    "              .assign(dv=lambda df: df.close.mul(df.volume)))\n",
    "\n",
    "    # select n assets with the highest average trading volume\n",
    "    # we are taking a shortcut to simplify; should select\n",
    "    # based on historical only, e.g. yearly rolling avg\n",
    "    most_traded = (df.groupby(level='ticker')\n",
    "                   .dv.mean()\n",
    "                   .nlargest(n=n).index)\n",
    "\n",
    "    df = (df.loc[idx[most_traded, :], 'close']\n",
    "          .unstack('ticker')\n",
    "          .ffill(limit=5)  # fill up to five values\n",
    "          .dropna(axis=1))  # remove assets with any missing values\n",
    "\n",
    "    df = remove_correlated_assets(df)\n",
    "    return remove_stationary_assets(df).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We store the intermediate result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:59:45.554530Z",
     "start_time": "2021-02-23T14:58:39.150056Z"
    }
   },
   "outputs": [],
   "source": [
    "for asset_class, n in [('etfs', 500), ('stocks', 250)]:\n",
    "    df = select_assets(asset_class=asset_class, n=n)\n",
    "    df.to_hdf('data.h5', f'{asset_class}/close')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get ticker dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:59:57.148863Z",
     "start_time": "2021-02-23T14:59:57.145495Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_ticker_dict():\n",
    "    with pd.HDFStore(STORE) as store:\n",
    "        return (pd.concat([\n",
    "            store['stooq/us/nyse/stocks/tickers'],\n",
    "            store['stooq/us/nyse/etfs/tickers'],\n",
    "            store['stooq/us/nasdaq/etfs/tickers'],\n",
    "            store['stooq/us/nasdaq/stocks/tickers']\n",
    "        ]).drop_duplicates().set_index('ticker').squeeze().to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T14:59:57.691206Z",
     "start_time": "2021-02-23T14:59:57.628201Z"
    }
   },
   "outputs": [],
   "source": [
    "names = get_ticker_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Correlation Clusters "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reload intermediate results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:00:54.264967Z",
     "start_time": "2021-02-23T15:00:54.248699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n",
      "Columns: 171 entries, AAPL.US to CLF.US\n",
      "dtypes: float64(171)\n",
      "memory usage: 3.3 MB\n"
     ]
    }
   ],
   "source": [
    "stocks = pd.read_hdf('data.h5', 'stocks/close')\n",
    "stocks.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:00:56.705358Z",
     "start_time": "2021-02-23T15:00:56.690013Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n",
      "Columns: 139 entries, SPY.US to DBB.US\n",
      "dtypes: float64(139)\n",
      "memory usage: 2.7 MB\n"
     ]
    }
   ],
   "source": [
    "etfs = pd.read_hdf('data.h5', 'etfs/close')\n",
    "etfs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:15.101618Z",
     "start_time": "2021-02-23T15:01:13.647425Z"
    }
   },
   "outputs": [],
   "source": [
    "tickers = {k: v for k, v in names.items() if k in etfs.columns.union(stocks.columns)}\n",
    "pd.Series(tickers).to_hdf('data.h5', 'tickers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:19.874810Z",
     "start_time": "2021-02-23T15:01:15.102722Z"
    }
   },
   "outputs": [],
   "source": [
    "corr = pd.DataFrame(index=stocks.columns)\n",
    "for etf, data in etfs.items():\n",
    "    corr[etf] = stocks.corrwith(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:19.888310Z",
     "start_time": "2021-02-23T15:01:19.875906Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 171 entries, AAPL.US to CLF.US\n",
      "Columns: 139 entries, SPY.US to DBB.US\n",
      "dtypes: float64(139)\n",
      "memory usage: 191.1+ KB\n"
     ]
    }
   ],
   "source": [
    "corr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:20.585582Z",
     "start_time": "2021-02-23T15:01:19.889466Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.clustermap(corr, cmap=cmap, center=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Candidate Selection using Heuristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computational Complexity: Comparing running times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we compare the running times of various cointegration tests. More specifically, we are running tests for a single asset vs. the remaining set of securities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:38.693161Z",
     "start_time": "2021-02-23T15:01:38.689811Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2516, 171), (2516, 139))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.shape, etfs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:38.714843Z",
     "start_time": "2021-02-23T15:01:38.696321Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n",
      "Columns: 171 entries, AAPL.US to CLF.US\n",
      "dtypes: float64(171)\n",
      "memory usage: 3.3 MB\n"
     ]
    }
   ],
   "source": [
    "stocks.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:38.732329Z",
     "start_time": "2021-02-23T15:01:38.716060Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2516 entries, 2010-01-04 to 2019-12-31\n",
      "Columns: 139 entries, SPY.US to DBB.US\n",
      "dtypes: float64(139)\n",
      "memory usage: 2.7 MB\n"
     ]
    }
   ],
   "source": [
    "etfs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:38.743544Z",
     "start_time": "2021-02-23T15:01:38.733468Z"
    }
   },
   "outputs": [],
   "source": [
    "security = etfs['AAXJ.US'].loc['2010': '2013']\n",
    "candidates = stocks.loc['2010': '2013']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:38.749550Z",
     "start_time": "2021-02-23T15:01:38.744629Z"
    }
   },
   "outputs": [],
   "source": [
    "security = security.div(security.iloc[0])\n",
    "candidates = candidates.div(candidates.iloc[0])\n",
    "spreads = candidates.sub(security, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:38.756941Z",
     "start_time": "2021-02-23T15:01:38.750525Z"
    }
   },
   "outputs": [],
   "source": [
    "n, m = spreads.shape\n",
    "X = np.ones(shape=(n, 2))\n",
    "X[:, 1] = np.arange(1, n+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Heuristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:44.686593Z",
     "start_time": "2021-02-23T15:01:38.757885Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "710 µs ± 5.42 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "np.linalg.inv(X.T @ X) @ X.T @ spreads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:53.021935Z",
     "start_time": "2021-02-23T15:01:44.688097Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.01 ms ± 21.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "spreads.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:01:55.542166Z",
     "start_time": "2021-02-23T15:01:53.023518Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31 ms ± 155 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "candidates.corrwith(security)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cointegration Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:06:17.974043Z",
     "start_time": "2021-02-23T15:01:55.544702Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33.6 s ± 6.43 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "for candidate, prices in candidates.items():\n",
    "    df = pd.DataFrame({'s1': security,\n",
    "                       's2': prices})\n",
    "    var = VAR(df.values)\n",
    "    lags = var.select_order()\n",
    "    k_ar_diff = lags.selected_orders['aic']\n",
    "    coint_johansen(df, det_order=0, k_ar_diff=k_ar_diff)\n",
    "    coint(security, prices, trend='c')[:2]\n",
    "    coint(prices, security, trend='c')[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, cointegration tests are significantly more costly. It would be great if the heuristics worked just as well, or at least 'good enough'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute Heuristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `compute_pair_metrics()` computes the following distance metrics for over 23,000 pairs of\n",
    "stocks and Exchange Traded Funds (ETFs) for 2010-14 and 2015-19:\n",
    "\n",
    "- The **drift of the spread**, defined as a linear regression of a time trend on the spread\n",
    "- The spread's  **volatility**\n",
    "- The **correlations** between the normalized price series and between their returns\n",
    "\n",
    "Low drift and volatility, as well as high correlation, are simple proxies for cointegration. \n",
    "\n",
    "To evaluate the predictive power of these heuristics, we also run Engle-Granger and Johansen **cointegration tests** using `statsmodels` for the preceding pairs. This takes place in the loop in the second half of `compute_pair_metrics()`.\n",
    "\n",
    "We first estimate the optimal number of lags that we need to specify for the Johansen test. For both tests, we assume that the cointegrated series (the spread) may have an intercept different from zero but no trend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:51.082000Z",
     "start_time": "2021-02-23T15:13:51.071380Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_pair_metrics(security, candidates):\n",
    "    security = security.div(security.iloc[0])\n",
    "    ticker = security.name\n",
    "    candidates = candidates.div(candidates.iloc[0])\n",
    "    spreads = candidates.sub(security, axis=0)\n",
    "    n, m = spreads.shape\n",
    "    X = np.ones(shape=(n, 2))\n",
    "    X[:, 1] = np.arange(1, n + 1)\n",
    "    \n",
    "    # compute drift\n",
    "    drift = ((np.linalg.inv(X.T @ X) @ X.T @ spreads).iloc[1]\n",
    "             .to_frame('drift'))\n",
    "    \n",
    "    # compute volatility\n",
    "    vol = spreads.std().to_frame('vol')\n",
    "    \n",
    "    # return correlation\n",
    "    corr_ret = (candidates.pct_change()\n",
    "                .corrwith(security.pct_change())\n",
    "                .to_frame('corr_ret'))\n",
    "    \n",
    "    # normalized price series correlation\n",
    "    corr = candidates.corrwith(security).to_frame('corr')\n",
    "    metrics = drift.join(vol).join(corr).join(corr_ret).assign(n=n)\n",
    "    \n",
    "    tests = []\n",
    "    # run cointegration tests\n",
    "    for candidate, prices in tqdm(candidates.items()):\n",
    "        df = pd.DataFrame({'s1': security, 's2': prices})\n",
    "        var = VAR(df.values)\n",
    "        lags = var.select_order() # select VAR order\n",
    "        k_ar_diff = lags.selected_orders['aic']\n",
    "        # Johansen Test with constant Term and estd. lag order\n",
    "        cj0 = coint_johansen(df, det_order=0, k_ar_diff=k_ar_diff)\n",
    "        # Engle-Granger Tests\n",
    "        t1, p1 = coint(security, prices, trend='c')[:2]\n",
    "        t2, p2 = coint(prices, security, trend='c')[:2]\n",
    "        tests.append([ticker, candidate, t1, p1, t2, p2, \n",
    "                      k_ar_diff, *cj0.lr1])\n",
    "    columns = ['s1', 's2', 't1', 'p1', 't2', 'p2', 'k_ar_diff', 'trace0', 'trace1']\n",
    "    tests = pd.DataFrame(tests, columns=columns).set_index('s2')\n",
    "    return metrics.join(tests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:44:09.355159Z",
     "start_time": "2021-02-23T15:16:00.355882Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "spreads = []\n",
    "start = 2010\n",
    "stop = 2019\n",
    "etf_candidates = etfs.loc[str(start): str(stop), :]\n",
    "stock_candidates = stocks.loc[str(start): str(stop), :]\n",
    "s = time()\n",
    "for i, (etf_ticker, etf_prices) in enumerate(etf_candidates.items(), 1):\n",
    "    df = compute_pair_metrics(etf_prices, stock_candidates)\n",
    "    spreads.append(df.set_index('s1', append=True))\n",
    "    if i % 10 == 0:\n",
    "        print(f'\\n{i:>3} {time() - s:.1f}\\n')\n",
    "        s = time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.577216Z",
     "start_time": "2021-02-23T15:01:38.727Z"
    }
   },
   "outputs": [],
   "source": [
    "names = get_ticker_dict()\n",
    "spreads = pd.concat(spreads)\n",
    "spreads.index.names = ['s2', 's1']\n",
    "spreads = spreads.swaplevel()\n",
    "spreads['name1'] = spreads.index.get_level_values('s1').map(names)\n",
    "spreads['name2'] = spreads.index.get_level_values('s2').map(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.578888Z",
     "start_time": "2021-02-23T15:01:38.730Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads['t'] = spreads[['t1', 't2']].min(axis=1)\n",
    "spreads['p'] = spreads[['p1', 'p2']].min(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Engle-Granger vs Johansen: how do their findings compare?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check for the significance of the cointegration tests, we compare the Johansen trace statistic for rank 0 and 1 to their respective critical values and obtain the Engle-Granger p-value.\n",
    "\n",
    "We follow the recommendation by Gonzalo and Lee (1998) to apply both tests and accept pairs where they agree. The authors suggest additional due diligence in case of disagreement, which we are going to skip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.580513Z",
     "start_time": "2021-02-23T15:01:38.733Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads['trace_sig'] = ((spreads.trace0 > trace0_cv) &\n",
    "                        (spreads.trace1 > trace1_cv)).astype(int)\n",
    "spreads['eg_sig'] = (spreads.p < .05).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the over 46,000 pairs across both sample periods, the Johansen test considers 3.2 percent of the relationships as significant, while the Engle-Granger considers 6.5 percent. They agree on 366 pairs (results may change with new data downloaded from stooq)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.582004Z",
     "start_time": "2021-02-23T15:01:38.736Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.crosstab(spreads.eg_sig, spreads.trace_sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.583606Z",
     "start_time": "2021-02-23T15:01:38.739Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads['coint'] = (spreads.trace_sig & spreads.eg_sig).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.585193Z",
     "start_time": "2021-02-23T15:01:38.741Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.587625Z",
     "start_time": "2021-02-23T15:01:38.744Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads = spreads.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.588996Z",
     "start_time": "2021-02-23T15:01:38.746Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.scatterplot(x=np.log1p(spreads.t.abs()), \n",
    "                y=np.log1p(spreads.trace1), \n",
    "                hue='coint', data=spreads[spreads.trace0>trace0_cv]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.589724Z",
     "start_time": "2021-02-23T15:01:38.749Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads.to_hdf('heuristics.h5', 'spreads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.590297Z",
     "start_time": "2021-02-23T15:01:38.751Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads = pd.read_hdf('heuristics.h5', 'spreads')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate Heuristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.590796Z",
     "start_time": "2021-02-23T15:01:38.755Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads.drift = spreads.drift.abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.591326Z",
     "start_time": "2021-02-23T15:01:38.757Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.crosstab(spreads.eg_sig, spreads.trace_sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.591967Z",
     "start_time": "2021-02-23T15:01:38.760Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', lambda x: f'{x:.2%}')\n",
    "pd.crosstab(spreads.eg_sig, spreads.trace_sig, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.592458Z",
     "start_time": "2021-02-23T15:01:38.762Z"
    }
   },
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(ncols=4, figsize=(20, 5))\n",
    "for i, heuristic in enumerate(['drift', 'vol', 'corr', 'corr_ret']):\n",
    "    sns.boxplot(x='coint', y=heuristic, data=spreads, ax=axes[i])\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How well do the heuristics predict significant cointegration?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we compare the distributions of the heuristics for series that are cointegrated according to both tests with the remainder that is not, volatility and drift are indeed lower (in absolute terms). Figure 9.14 shows that the picture is less clear for the two correlation measures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.592973Z",
     "start_time": "2021-02-23T15:01:38.765Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads.groupby(spreads.coint)['drift', 'vol', 'corr'].describe().stack(level=0).swaplevel().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.593502Z",
     "start_time": "2021-02-23T15:01:38.770Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads.coint.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate the predictive accuracy of the heuristics, we first run a logistic regression model with these features to predict significant cointegration. It achieves an area-under-the-curve (AUC) cross-validation score of 0.815; excluding the correlation metrics, it still scores 0.804. A decision tree does slightly better at AUC=0.821, with or without the correlation features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.594154Z",
     "start_time": "2021-02-23T15:01:38.777Z"
    }
   },
   "outputs": [],
   "source": [
    "y = spreads.coint\n",
    "X = spreads[['drift', 'vol', 'corr', 'corr_ret']]\n",
    "# X = spreads[['drift', 'vol']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.594658Z",
     "start_time": "2021-02-23T15:01:38.782Z"
    }
   },
   "outputs": [],
   "source": [
    "kf = StratifiedKFold(n_splits=5, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.595242Z",
     "start_time": "2021-02-23T15:01:38.787Z"
    }
   },
   "outputs": [],
   "source": [
    "log_reg = LogisticRegressionCV(Cs=np.logspace(-10, 10, 21), \n",
    "                               class_weight='balanced',\n",
    "                               scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.595833Z",
     "start_time": "2021-02-23T15:01:38.792Z"
    }
   },
   "outputs": [],
   "source": [
    "log_reg.fit(X=X, y=y)\n",
    "Cs = log_reg.Cs_\n",
    "scores = pd.DataFrame(log_reg.scores_[True], columns=Cs).mean()\n",
    "scores.plot(logx=True);\n",
    "f'C:{np.log10(scores.idxmax()):.2f}, AUC: {scores.max():.2%}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.596334Z",
     "start_time": "2021-02-23T15:01:38.797Z"
    }
   },
   "outputs": [],
   "source": [
    "log_reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.596850Z",
     "start_time": "2021-02-23T15:01:38.801Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = log_reg.predict_proba(X)[:, 1]\n",
    "confusion_matrix(y_true=spreads.coint, y_pred=(y_pred>.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.597418Z",
     "start_time": "2021-02-23T15:01:38.807Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads.assign(y_pred=log_reg.predict_proba(X)[:, 1]).groupby(spreads.coint).y_pred.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not least due to the strong class imbalance, there are large numbers of false positives:\n",
    "correctly identifying 80 percent of the 366 cointegrated pairs implies over 16,500 false positives, but eliminates almost 30,000 of the candidates. See the notebook cointegration_\n",
    "tests for additional detail.\n",
    "\n",
    "The **key takeaway** is that distance heuristics can help screen a large universe more  efficiently, but this comes at a cost of missing some cointegrated pairs and still requires\n",
    "substantial testing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.597962Z",
     "start_time": "2021-02-23T15:01:38.813Z"
    }
   },
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier(class_weight='balanced')\n",
    "decision_tree = GridSearchCV(model,\n",
    "                             param_grid={'max_depth': list(range(1, 10))},\n",
    "                             cv=5,\n",
    "                             scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.598628Z",
     "start_time": "2021-02-23T15:01:38.817Z"
    }
   },
   "outputs": [],
   "source": [
    "decision_tree.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.599171Z",
     "start_time": "2021-02-23T15:01:38.822Z"
    }
   },
   "outputs": [],
   "source": [
    "f'{decision_tree.best_score_:.2%}, Depth: {decision_tree.best_params_[\"max_depth\"]}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.599733Z",
     "start_time": "2021-02-23T15:01:38.828Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.Series(data=decision_tree.best_estimator_.feature_importances_, \n",
    "          index=X.columns).sort_values().plot.barh(title='Feature Importance')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.600289Z",
     "start_time": "2021-02-23T15:01:38.832Z"
    }
   },
   "outputs": [],
   "source": [
    "spreads.assign(y_pred=decision_tree.predict_proba(X)[:, 1]).groupby(spreads.coint).y_pred.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T15:13:20.600835Z",
     "start_time": "2021-02-23T15:01:38.838Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.catplot(x='coint', \n",
    "            y='y_pred', \n",
    "            data=spreads.assign(y_pred=decision_tree.predict_proba(X)[:, 1]), \n",
    "            kind='box');"
   ]
  }
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